migrate-postgres-tables-to-hypertables
Comprehensive guide for migrating PostgreSQL tables to TimescaleDB hypertables with optimal configuration and performance validation
What this skill does
# PostgreSQL to TimescaleDB Hypertable Migration
Migrate identified PostgreSQL tables to TimescaleDB hypertables with optimal configuration, migration planning and validation.
**Prerequisites**: Tables already identified as hypertable candidates (use companion "find-hypertable-candidates" skill if needed).
## Step 1: Optimal Configuration
### Partition Column Selection
```sql
-- Find potential partition columns
SELECT column_name, data_type, is_nullable
FROM information_schema.columns
WHERE table_name = 'your_table_name'
AND data_type IN ('timestamp', 'timestamptz', 'bigint', 'integer', 'date')
ORDER BY ordinal_position;
```
**Requirements:** Time-based (TIMESTAMP/TIMESTAMPTZ/DATE) or sequential integer (INT/BIGINT)
Should represent when the event actually occurred or sequential ordering.
**Common choices:**
- `timestamp`, `created_at`, `event_time` - when event occurred
- `id`, `sequence_number` - auto-increment (for sequential data without timestamps)
- `ingested_at` - less ideal, only if primary query dimension
- `updated_at` - AVOID (records updated out of order, breaks chunk distribution) unless primary query dimension
#### Special Case: table with BOTH ID AND Timestamp
When table has sequential ID (PK) AND timestamp that correlate:
```sql
-- Partition by ID, enable minmax sparse indexes on timestamp
SELECT create_hypertable('orders', 'id', chunk_time_interval => 1000000);
ALTER TABLE orders SET (
timescaledb.sparse_index = 'minmax(created_at),...'
);
```
Sparse indexes on time column enable skipping compressed blocks outside queried time ranges.
Use when: ID correlates with time (newer records have higher IDs), need ID-based lookups, time queries also common
### Chunk Interval Selection
```sql
-- Ensure statistics are current
ANALYZE your_table_name;
-- Estimate index size per time unit
WITH time_range AS (
SELECT
MIN(timestamp_column) as min_time,
MAX(timestamp_column) as max_time,
EXTRACT(EPOCH FROM (MAX(timestamp_column) - MIN(timestamp_column)))/3600 as total_hours
FROM your_table_name
),
total_index_size AS (
SELECT SUM(pg_relation_size(indexname::regclass)) as total_index_bytes
FROM pg_stat_user_indexes
WHERE schemaname||'.'||tablename = 'your_schema.your_table_name'
)
SELECT
pg_size_pretty(tis.total_index_bytes / tr.total_hours) as index_size_per_hour
FROM time_range tr, total_index_size tis;
```
**Target:** Indexes of recent chunks < 25% of RAM
**Default:** IMPORTANT: Keep default of 7 days if unsure
**Range:** 1 hour minimum, 30 days maximum
**Example:** 32GB RAM → target 8GB for recent indexes. If index_size_per_hour = 200MB:
- 1 hour chunks: 200MB chunk index size × 40 recent = 8GB ✓
- 6 hour chunks: 1.2GB chunk index size × 7 recent = 8.4GB ✓
- 1 day chunks: 4.8GB chunk index size × 2 recent = 9.6GB ⚠️
Choose largest interval keeping 2+ recent chunk indexes under target.
### Primary Key/ Unique Constraints Compatibility
```sql
-- Check existing primary key/ unique constraints
SELECT conname, pg_get_constraintdef(oid) as definition
FROM pg_constraint
WHERE conrelid = 'your_table_name'::regclass AND contype = 'p' OR contype = 'u';
```
**Rules:** PK/UNIQUE must include partition column
**Actions:**
1. **No PK/UNIQUE:** No changes needed
2. **PK/UNIQUE includes partition column:** No changes needed
3. **PK/UNIQUE excludes partition column:** ⚠️ **ASK USER PERMISSION** to modify PK/UNIQUE
**Example: user prompt if needed:**
> "Primary key (id) doesn't include partition column (timestamp). Must modify to PRIMARY KEY (id, timestamp) to convert to hypertable. This may break application code. Is this acceptable?"
> "Unique constraint (id) doesn't include partition column (timestamp). Must modify to UNIQUE (id, timestamp) to convert to hypertable. This may break application code. Is this acceptable?"
If the user accepts, modify the constraint:
```sql
BEGIN;
ALTER TABLE your_table_name DROP CONSTRAINT existing_pk_name;
ALTER TABLE your_table_name ADD PRIMARY KEY (existing_columns, partition_column);
COMMIT;
```
If the user does not accept, you should NOT migrate the table.
IMPORTANT: DO NOT modify the primary key/unique constraint without user permission.
### Compression Configuration
For detailed segment_by and order_by selection, see "setup-timescaledb-hypertables" skill. Quick reference:
**segment_by:** Most common WHERE filter with >100 rows per value per chunk
- IoT: `device_id`
- Finance: `symbol`
- Analytics: `user_id` or `session_id`
```sql
-- Analyze cardinality for segment_by selection
SELECT column_name, COUNT(DISTINCT column_name) as unique_values,
ROUND(COUNT(*)::float / COUNT(DISTINCT column_name), 2) as avg_rows_per_value
FROM your_table_name GROUP BY column_name;
```
**order_by:** Usually `timestamp DESC`. The (segment_by, order_by) combination should form a natural time-series progression.
- If column has <100 rows/chunk (too low for segment_by), prepend to order_by: `order_by='low_density_col, timestamp DESC'`
**sparse indexes:** add minmax on the columns that are used in the WHERE clauses but are not in the segment_by or order_by. Use minmax for columns used in range queries.
```sql
ALTER TABLE your_table_name SET (
timescaledb.enable_columnstore,
timescaledb.segmentby = 'entity_id',
timescaledb.orderby = 'timestamp DESC'
timescaledb.sparse_index = 'minmax(value_1),...'
);
-- Compress after data unlikely to change (adjust `after` parameter based on update patterns)
CALL add_columnstore_policy('your_table_name', after => INTERVAL '7 days');
```
## Step 2: Migration Planning
### Pre-Migration Checklist
- [ ] Partition column selected
- [ ] Chunk interval calculated (or using default)
- [ ] PK includes partition column OR user approved modification
- [ ] No Hypertable→Hypertable foreign keys
- [ ] Unique constraints include partition column
- [ ] Created compression configuration (segment_by, order_by, sparse indexes, compression policy)
- [ ] Maintenance window scheduled / backup created.
### Migration Options
#### Option 1: In-Place (Tables < 1GB)
```sql
-- Enable extension
CREATE EXTENSION IF NOT EXISTS timescaledb;
-- Convert to hypertable (locks table)
SELECT create_hypertable(
'your_table_name',
'timestamp_column',
chunk_time_interval => INTERVAL '7 days',
if_not_exists => TRUE
);
-- Configure compression
ALTER TABLE your_table_name SET (
timescaledb.enable_columnstore,
timescaledb.segmentby = 'entity_id',
timescaledb.orderby = 'timestamp DESC',
timescaledb.sparse_index = 'minmax(value_1),...'
);
-- Adjust `after` parameter based on update patterns
CALL add_columnstore_policy('your_table_name', after => INTERVAL '7 days');
```
#### Option 2: Blue-Green (Tables > 1GB)
```sql
-- 1. Create new hypertable
CREATE TABLE your_table_name_new (LIKE your_table_name INCLUDING ALL);
-- 2. Convert to hypertable
SELECT create_hypertable('your_table_name_new', 'timestamp_column');
-- 3. Configure compression
ALTER TABLE your_table_name_new SET (
timescaledb.enable_columnstore,
timescaledb.segmentby = 'entity_id',
timescaledb.orderby = 'timestamp DESC'
);
-- 4. Migrate data in batches
INSERT INTO your_table_name_new
SELECT * FROM your_table_name
WHERE timestamp_column >= '2024-01-01' AND timestamp_column < '2024-02-01';
-- Repeat for each time range
-- 4. Enter maintenance window and do the following:
-- 5. Pause modification of the old table.
-- 6. Copy over the most recent data from the old table to the new table.
-- 7. Swap tables
BEGIN;
ALTER TABLE your_table_name RENAME TO your_table_name_old;
ALTER TABLE your_table_name_new RENAME TO your_table_name;
COMMIT;
-- 8. Exit maintenance window.
-- 9. (sometime much later) Drop old table after validation
-- DROP TABLE your_table_name_old;
```
### Common Issues
#### Foreign Keys
```sql
-- Check foreign keys
SELECT conname, confrelid::regclass as referenced_table
FROM pg_constraint
WHERE (conrelid = 'youRelated in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.